Описание: Data Mining for Business Analytics: Concepts, Techniques, and Applications in XLMiner(R), Third Edition presents an applied approach to data mining and predictive analytics with clear exposition, hands-on exercises, and real-life case studies.
This book is about the definition of the Shannon measure of Information, and some derived quantities such as conditional information and mutual information. Unlike many books, which refer to the Shannon's Measure of information (SMI) as "Entropy," this book makes a clear distinction between the SMI and Entropy.
In the last chapter, Entropy is derived as a special case of SMI.
Ample examples are provided which help the reader in understanding the different concepts discussed in this book. As with previous books by the author, this book aims at a clear and mystery-free presentation of the central concept in Information theory -- the Shannon's Measure of Information.
This book presents the fundamental concepts of Information theory in a friendly-simple language and is devoid of all kinds of fancy and pompous statements made by authors of popular science books who write on this subject. It is unique in its presentation of Shannon's measure of information, and the clear distinction between this concept and the thermodynamic entropy.
Although some mathematical knowledge is required by the reader, the emphasis is on the concepts and their meaning rather on the mathematical details of the theory.
This book is about the definition of the Shannon measure of Information, and some derived quantities such as conditional information and mutual information. Unlike many books, which refer to the Shannon's Measure of information (SMI) as "Entropy," this book makes a clear distinction between the SMI and Entropy.
In the last chapter, Entropy is derived as a special case of SMI.
Ample examples are provided which help the reader in understanding the different concepts discussed in this book. As with previous books by the author, this book aims at a clear and mystery-free presentation of the central concept in Information theory -- the Shannon's Measure of Information.
This book presents the fundamental concepts of Information theory in a friendly-simple language and is devoid of all kinds of fancy and pompous statements made by authors of popular science books who write on this subject. It is unique in its presentation of Shannon's measure of information, and the clear distinction between this concept and the thermodynamic entropy.
Although some mathematical knowledge is required by the reader, the emphasis is on the concepts and their meaning rather on the mathematical details of the theory.
Автор: Bekos Michael A., Niedermann Benjamin, Nцllenburg Martin Название: External Labeling: Fundamental Concepts and Algorithmic Techniques ISBN: 1636392067 ISBN-13(EAN): 9781636392066 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 11227.00 р. Наличие на складе: Нет в наличии.
Описание:
This book focuses on techniques for automating the procedure of creating external labelings, also known as callout labelings. In this labeling type, the features within an illustration are connected by thin leader lines (called leaders) with their labels, which are placed in the empty space surrounding the image.
In general, textual labels describing graphical features in maps, technical illustrations (such as assembly instructions or cutaway illustrations), or anatomy drawings are an important aspect of visualization that convey information on the objects of the visualization and help the reader understand what is being displayed.
Most labeling techniques can be classified into two main categories depending on the "distance" of the labels to their associated features. Internal labels are placed inside or in the direct neighborhood of features, while external labels, which form the topic of this book, are placed in the margins outside the illustration, where they do not occlude the illustration itself. Both approaches form well-studied topics in diverse areas of computer science with several important milestones.
The goal of this book is twofold. The first is to serve as an entry point for the interested reader who wants to get familiar with the basic concepts of external labeling, as it introduces a unified and extensible taxonomy of labeling models suitable for a wide range of applications. The second is to serve as a point of reference for more experienced people in the field, as it brings forth a comprehensive overview of a wide range of approaches to produce external labelings that are efficient either in terms of different algorithmic optimization criteria or in terms of their usability in specific application domains. The book mostly concentrates on algorithmic aspects of external labeling, but it also presents various visual aspects that affect the aesthetic quality and usability of external labeling.
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